First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP4063664, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
SC2095, Degree course structure A.Y. 2014/15, A.Y. 2018/19
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICAL METHODS FOR FINANCE
Website of the academic structure
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge MAURO BERNARDI SECS-S/03
Other lecturers MICHELE COSTOLA

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/03 Statistics for Economics 9.0

Course unit organization
Period Second semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 2.0 12 38.0 No turn
Lecture 7.0 52 123.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
3 Commissione a.a.2018/19 01/10/2018 30/09/2019 BERNARDI MAURO (Presidente)
BISAGLIA LUISA (Membro Effettivo)
LISI FRANCESCO (Membro Effettivo)

Prerequisites: • Knowledge of models and methods for the statistical analysis of
stationary time series.
• Basic notions of probability calculus and random variables.
• Statistics;
• Basic notions on the statistical software R.
Target skills and knowledge: The primary goal of the course is to enhance the abilities of students
fo analyse financial time series and, in particular, their ability to build,
estimate and validate statistical and mathematical models for the analysis
of the temporal evolution of conditional moments, with the main
focus on forecasting. Applications in finance and financial econometrics
will be considered during the course. The last part of the course is
devoted to the discussion of the main mathematical properties of risk
management tools and methods.
Through a combination of lectures, PC exercises, and group homeworks
students should also develop personal and professional skills valued by
employers (e.g., time management, planning, collaboration, responsibility,
and integrity), and discover that success in the workplace requires
strong quantitative skills.
Examination methods: The final exam consists of two parts: a written (theoretical) part and
the practical part. The theoretical part consists of several questions and
it is intended to evaluate the students’ knowledge of the main statistical
models and methods introduced during the course and their properties.
The practical part at the computer laboratory, should be done with the
statistical softwares R or Phython and it is devoted to assess the ability
of the students to apply the models and methods to real datasets. The
final grade considers both the theoretical part (85%) and practical part
Course unit contents: • Introduction to the characteristics of financial time series mainly
using graphical analysis of real examples: stock prices and indexes,
foreign exchange rates, interest rates, options, futures,
• Introduction to the main stock indexes.
• Prices, returns and volatility: definition, measurement and analysis
of their characteristics.
• Introduction to the models for the analysis and prediction of the
volatility of financial time series: the class of ARCH–type models
• Inference for the class of ARCH–type models.
• Analysis of the characteristics of high frequency data (intraday
financial data).
• Introduction to financial risk measurement and management. Analysis
of the mathematical properties of the risk measures: VaR,
TCE and Expected Shortfall.
Planned learning activities and teaching methods: Active attendance, participation, and preparation are required. Students
are expected to attend every class, and they are strongly encouraged
to prepare every assignment. The softwares R and Phyton will be used to build, estimate and validate statistical models for financial
time series.
Because homework is a valuable and integral component of successful
statistical learning experiences, group homeworks (of about 4/5 people)
will be regularly assigned to provide students regular feedback about
their learning progress. Assignments that are submitted on a timely
basis will be returned approximately two weeks after their submission.
Homeworks are not mandatory, but students are strongly encouraged
to complete all the assignments.
Additional notes about suggested reading: Slides and handouts will be provided at the beginning of the course.
Further material and readings will be assigned during the course.
Textbooks (and optional supplementary readings)
  • Gallo, G.M. e Pacini, B., Metodi quantitativi per i mercati finanziari. Firenze: Carocci Editore, 2002. Cerca nel catalogo
  • Tsay R.S., Analysis of financial time series. New York: Wiley, 2010. 3rd Edition Cerca nel catalogo
  • Tsay, R.S., An introduction to analysis of financial data with R. New York: Wiley, 2013. Cerca nel catalogo
  • Francq, C. and Zakoian, J.M., GARCH Models: Structure, Statistical inference and Financial applications. New York: Wiley, 2010. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Problem based learning
  • Case study
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Quality Education